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print('hello')
#> [1] "hello"1 Introduction
- Overview and Motivation
- Related Work
- Research questions
1.1 Setup
1.1.1 Libraries
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# Python code
import numpy as np
print(np.mean([10, 20, 30, 40, 50]))
#> 30.02 Data
- Sources
- Description
- Wrangling/cleaning
- Spotting mistakes and missing data (could be part of EDA too)
- Listing anomalies and outliers (could be part of EDA too)
2.1 Wrangling and Cleaning
- ajouter source
- ajouter description
- expliquer blabla
- Explain why we remove NA from m2 column.
- Explain …
2.1.1 Raw dataset
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properties <- read.csv(file.path(here(),"data/properties.csv"))
# show 1000 first rows of properties using reactable
reactable(head(properties, 1000))2.1.2 Cleaning
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# Identify values causing the issue
problematic_values <- properties$number_of_rooms[is.na(as.numeric(properties$number_of_rooms))]
#> Warning: NAs introduced by coercion
# Replace non-numeric values with NA
properties$number_of_rooms <- as.numeric(gsub("[^0-9.]", "", properties$number_of_rooms))
# Remove non-numeric characters and convert to numeric
properties$price <- as.numeric(gsub("[^0-9]", "", properties$price))
# Subset the dataset to exclude rows with price < 20000
properties <- properties[properties$price >= 20000, ]
# Subset the dataset to exclude rows with numbers of rooms < 25
properties <- properties[properties$number_of_rooms <25, ]
# Replace incomplete addresses
properties$address <- gsub("^\\W*[.,0-]\\W*", "", properties$address)
properties_filtered <- na.omit(properties)
properties_filtered$year_category <- substr(properties_filtered$year_category, 1, 9)
# Assuming 'year_category' is a column in the 'properties' dataset
properties_filtered$year_category <- as.factor(properties_filtered$year_category)
# Preprocess the number_of_rooms column
properties_filtered$number_of_rooms <- as.character(properties_filtered$number_of_rooms)
properties_filtered$number_of_rooms <- gsub("\\D", "", properties_filtered$number_of_rooms) # Remove non-numeric characters
properties_filtered$number_of_rooms <- as.numeric(properties_filtered$number_of_rooms) # Convert to numeric
properties_filtered$number_of_rooms <- trunc(properties_filtered$number_of_rooms) # Truncate non-integer values
# remove m^2 from column 'square_meters'
properties_filtered$square_meters <- as.numeric(gsub("\\D", "", properties_filtered$square_meters))
# print how many NA observations left in square_meters
print(sum(is.na(properties_filtered$square_meters)))
#> [1] 988
# remove NA
properties_filtered <- properties_filtered[!is.na(properties_filtered$square_meters),]
# add majuscule to canton
properties_filtered$canton <- tools::toTitleCase(properties_filtered$canton)
# show 100 first row of cleaned dataset using reactable
reactable(head(properties_filtered, 100))2.1.3 AMTOVZ_CSV_LV95 Data
- ajouter source
- ajouter description
- expliquer blabla
2.1.3.1 Creating Variable zip_code and merging with AMTOVZ_CSV_LV95
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df <- properties_filtered
#the address column is like : '1844 Villeneuve VD' and has zip code number in it
#taking out the zip code number and creating a new column 'zip_code'
#the way to identify the zip code is to identify numbers that are 4 digits long
df$zip_code <- as.numeric(gsub("\\D", "", df$address))
#removing the first two number of zip code has more than 4 number
df$zip_code <- ifelse(df$zip_code > 9999, df$zip_code %% 10000, df$zip_code)2.1.3.2 Using AMTOVZ_CSV_LV95 to get the city and canton from the zip code
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#read .csv AMTOVZ_CSV_LV95
amto <- read.csv(file.path(here(),"data/AMTOVZ_CSV_LV95.csv"), sep = ";")
#creating a new dataframe with 'Ortschaftsname' as 'City'Place_name', 'PLZ' as 'zip_code' and 'KantonskÃ.rzel' as 'Canton_code'
amto_df <- data.frame(City=amto$Ortschaftsname, zip_code=amto$PLZ, Canton_code=amto$Kantonskürzel)
# display 100 first rows of atmo_df using reactable
reactable::reactable(head(amto_df, 1000))Click to show code
#merge the two dataframes 'df' and 'amto_df' on 'zip_code'
df <- merge(df, amto_df, by='zip_code', all.x=TRUE)
#check if there are nan in city
df[is.na(df$City),]
#> zip_code price number_of_rooms square_meters
#> 1 25 2200000 65 165
#> 2 25 2200000 10 263
#> 3 26 655000 35 66
#> 4 26 1995000 75 180
#> 5 322 870000 25 59
#> 6 322 880000 25 55
#> 7 322 975000 35 56
#> 336 1014 1510000 55 146
#> 2127 1200 16092000 7 400
#> 2128 1200 3285450 5 230
#> 2129 1200 679000 55 142
#> 11521 1919 785000 35 103
#> 11522 1919 1908000 65 210
#> 11523 1919 2558620 55 270
#> 11524 1919 1065000 45 130
#> 17583 2500 1100000 5 154
#> 17584 2500 420000 45 115
#> 17585 2500 885500 55 130
#> 17586 2500 872500 45 144
#> 17587 2500 872500 45 138
#> 17588 2500 887500 55 130
#> 17589 2500 870500 45 125
#> 17590 2500 892500 45 144
#> 17591 2500 885500 55 130
#> 17592 2500 1050000 45 121
#> 17593 2500 877500 45 138
#> 17594 2500 887500 45 144
#> 17595 2500 1450000 55 198
#> 19178 3000 820000 55 165
#> 19179 3000 1140000 35 115
#> 19180 3000 1090000 35 115
#> 19181 3000 1090000 55 193
#> 19182 3000 920000 45 157
#> 19183 3000 1090000 55 193
#> 19184 3000 1590000 55 330
#> 19185 3000 720000 35 102
#> 19186 3000 920000 45 157
#> 26756 4000 180000 3 70
#> 26757 4000 975000 45 125
#> 26758 4000 2100000 65 360
#> 30233 5201 725000 35 95
#> 31403 6000 695000 45 133
#> 33026 6511 440000 2 64
#> 33433 6547 15000000 75 220
#> 34562 6602 2800000 75 242
#> 34563 6602 2800000 65 250
#> 34564 6602 270000 15 28
#> 34565 6602 450000 35 75
#> 34566 6604 1990000 45 220
#> 34567 6604 2668590 55 290
#> 34568 6604 760000 35 78
#> 39888 6901 3660930 45 290
#> 39889 6901 3660930 45 290
#> 39890 6903 790000 35 105
#> 39891 6907 995000 45 114
#> 39892 6907 995000 45 114
#> 39893 6911 469350 55 140
#> 39894 6911 610000 35 103
#> 39895 6911 660000 75 200
#> 39896 6911 737550 45 82
#> 41884 7133 2266290 55 160
#> 41901 7135 2690000 85 236
#> 42256 8000 2100000 45 152
#> 42257 8000 1650000 45 142
#> 42258 8000 925000 35 102
#> 42259 8000 1650000 45 142
#> 42260 8000 1150000 45 128
#> 42261 8000 1450000 55 143
#> 42262 8000 1990000 55 200
#> 42263 8000 1990000 55 200
#> 42264 8000 975000 45 122
#> 42265 8000 2495000 55 482
#> 43220 8238 245000 2 49
#> 43955 8423 2110000 65 204
#> 43956 8423 2190000 55 167
#> 46558 9241 545000 45 100
#> 46559 9241 730840 55 130
#> address
#> 1 1000 Lausanne 25
#> 2 1000 Lausanne 25
#> 3 1000 Lausanne 26
#> 4 Lausanne 26, 1000 Lausanne 26
#> 5 Via Cuolm Liung 30d, 7032 Laax GR 2
#> 6 7032 Laax GR 2
#> 7 Via Murschetg 29, 7032 Laax GR 2
#> 336 1014 Lausanne
#> 2127 1200 Genève
#> 2128 1200 Genève
#> 2129 Chemin des pralets, 74100 Etrembières, 1200 Genève
#> 11521 1919 Martigny
#> 11522 1919 Martigny
#> 11523 1919 Martigny
#> 11524 1919 Martigny
#> 17583 2500 Biel/Bienne
#> 17584 2500 Biel/Bienne
#> 17585 2500 Biel/Bienne
#> 17586 2500 Biel/Bienne
#> 17587 2500 Biel/Bienne
#> 17588 2500 Biel/Bienne
#> 17589 2500 Biel/Bienne
#> 17590 2500 Biel/Bienne
#> 17591 2500 Biel/Bienne
#> 17592 Hohlenweg 11b, 2500 Biel/Bienne
#> 17593 2500 Biel/Bienne
#> 17594 2500 Biel/Bienne
#> 17595 2500 Bienne
#> 19178 3000 Bern
#> 19179 3000 Bern
#> 19180 3000 Bern
#> 19181 3000 Bern
#> 19182 3000 Bern
#> 19183 3000 Bern
#> 19184 3000 Bern
#> 19185 3000 Bern
#> 19186 3000 Bern
#> 26756 Lörrach Brombach Steinsack 6, 4000 Basel
#> 26757 4000 Basel
#> 26758 4000 Basel
#> 30233 5201 Brugg AG
#> 31403 in TRIENGEN, ca. 20 min. bei Luzern, 6000 Luzern
#> 33026 6511 Cadenazzo
#> 33433 Augio 1F, 6547 Augio
#> 34562 6602 Muralto
#> 34563 6602 Muralto
#> 34564 6602 Muralto
#> 34565 Via Bacilieri 2, 6602 Muralto
#> 34566 6604 Solduno
#> 34567 6604 Solduno
#> 34568 6604 Locarno
#> 39888 6901 Lugano
#> 39889 6901 Lugano
#> 39890 6903 Lugano
#> 39891 6907 MASSAGNO
#> 39892 6907 MASSAGNO
#> 39893 6911 Campione d'Italia
#> 39894 6911 Campione d'Italia
#> 39895 6911 Campione d'Italia
#> 39896 6911 Campione d'Italia
#> 41884 Inder Platenga 34, 7133 Obersaxen
#> 41901 7135 Fideris
#> 42256 8000 Zürich
#> 42257 8000 Zürich
#> 42258 8000 Zürich
#> 42259 8000 Zürich
#> 42260 8000 Zürich
#> 42261 8000 Zürich
#> 42262 8000 Zürich
#> 42263 8000 Zürich
#> 42264 8000 Zürich
#> 42265 8000 Zürich
#> 43220 Stemmerstrasse 14, 8238 Büsingen am Hochrhein
#> 43955 Chüngstrasse 60, 8423 Embrach
#> 43956 Chüngstrasse 48, 8423 Embrach
#> 46558 9241 Kradolf
#> 46559 9241 Kradolf
#> canton property_type floor year_category City
#> 1 Vaud Villa 2006-2010 <NA>
#> 2 Vaud Single house 1919-1945 <NA>
#> 3 Vaud Apartment noteg 2016-2024 <NA>
#> 4 Vaud Villa 1961-1970 <NA>
#> 5 Grisons Apartment eg 2016-2024 <NA>
#> 6 Grisons Apartment noteg 2016-2024 <NA>
#> 7 Grisons Apartment noteg 2011-2015 <NA>
#> 336 Vaud Apartment eg 2011-2015 <NA>
#> 2127 Geneva Single house 2011-2015 <NA>
#> 2128 Geneva Bifamiliar house 1981-1990 <NA>
#> 2129 Geneva Bifamiliar house 2016-2024 <NA>
#> 11521 Valais Apartment noteg 2016-2024 <NA>
#> 11522 Valais Apartment noteg 2016-2024 <NA>
#> 11523 Valais Attic flat noteg 2016-2024 <NA>
#> 11524 Valais Apartment noteg 2016-2024 <NA>
#> 17583 Bern Single house 2001-2005 <NA>
#> 17584 Bern Apartment noteg 1971-1980 <NA>
#> 17585 Bern Villa 2016-2024 <NA>
#> 17586 Bern Villa 2016-2024 <NA>
#> 17587 Bern Single house 2016-2024 <NA>
#> 17588 Bern Single house 2016-2024 <NA>
#> 17589 Bern Single house 2016-2024 <NA>
#> 17590 Bern Single house 2016-2024 <NA>
#> 17591 Bern Single house 2016-2024 <NA>
#> 17592 Bern Single house 2001-2005 <NA>
#> 17593 Bern Single house 2016-2024 <NA>
#> 17594 Bern Single house 2016-2024 <NA>
#> 17595 Bern Single house 2016-2024 <NA>
#> 19178 Bern Apartment noteg 2016-2024 <NA>
#> 19179 Bern Apartment eg 2016-2024 <NA>
#> 19180 Bern Apartment eg 2016-2024 <NA>
#> 19181 Bern Roof flat noteg 2016-2024 <NA>
#> 19182 Bern Apartment noteg 2016-2024 <NA>
#> 19183 Bern Apartment noteg 2016-2024 <NA>
#> 19184 Bern Apartment noteg 1991-2000 <NA>
#> 19185 Bern Apartment eg 2016-2024 <NA>
#> 19186 Bern Duplex noteg 2016-2024 <NA>
#> 26756 Basel-Stadt Single house 1961-1970 <NA>
#> 26757 Basel-Stadt Single house 2016-2024 <NA>
#> 26758 Basel-Stadt Villa 2016-2024 <NA>
#> 30233 Aargau Apartment noteg 2016-2024 <NA>
#> 31403 Lucerne Apartment noteg 1991-2000 <NA>
#> 33026 Ticino Apartment noteg 2016-2024 <NA>
#> 33433 Grisons Single house 2016-2024 <NA>
#> 34562 Ticino Single house 1981-1990 <NA>
#> 34563 Ticino Single house 1981-1990 <NA>
#> 34564 Ticino Apartment eg 1961-1970 <NA>
#> 34565 Ticino Apartment noteg 1946-1960 <NA>
#> 34566 Ticino Attic flat noteg 2011-2015 <NA>
#> 34567 Ticino Apartment noteg 2011-2015 <NA>
#> 34568 Ticino Apartment noteg 2011-2015 <NA>
#> 39888 Ticino Attic flat noteg 2011-2015 <NA>
#> 39889 Ticino Apartment noteg 2011-2015 <NA>
#> 39890 Ticino Apartment noteg 2006-2010 <NA>
#> 39891 Ticino Apartment noteg 2016-2024 <NA>
#> 39892 Ticino Apartment noteg 2016-2024 <NA>
#> 39893 Ticino Apartment noteg 1946-1960 <NA>
#> 39894 Ticino Apartment eg 1946-1960 <NA>
#> 39895 Ticino Single house 1971-1980 <NA>
#> 39896 Ticino Apartment noteg 1991-2000 <NA>
#> 41884 Grisons Single house 2006-2010 <NA>
#> 41901 Grisons Single house 0-1919 <NA>
#> 42256 Zurich Apartment noteg 2016-2024 <NA>
#> 42257 Zurich Attic flat noteg 2016-2024 <NA>
#> 42258 Zurich Apartment noteg 2016-2024 <NA>
#> 42259 Zurich Apartment noteg 2016-2024 <NA>
#> 42260 Zurich Apartment noteg 2016-2024 <NA>
#> 42261 Zurich Apartment eg 2016-2024 <NA>
#> 42262 Zurich Apartment noteg 2006-2010 <NA>
#> 42263 Zurich Attic flat noteg 2006-2010 <NA>
#> 42264 Zurich Single house 2016-2024 <NA>
#> 42265 Zurich Apartment noteg 0-1919 <NA>
#> 43220 Schaffhausen Apartment noteg 1961-1970 <NA>
#> 43955 Zurich Bifamiliar house 2016-2024 <NA>
#> 43956 Zurich Single house 2016-2024 <NA>
#> 46558 Thurgau Apartment noteg 1991-2000 <NA>
#> 46559 Thurgau Apartment noteg 1991-2000 <NA>
#> Canton_code
#> 1 <NA>
#> 2 <NA>
#> 3 <NA>
#> 4 <NA>
#> 5 <NA>
#> 6 <NA>
#> 7 <NA>
#> 336 <NA>
#> 2127 <NA>
#> 2128 <NA>
#> 2129 <NA>
#> 11521 <NA>
#> 11522 <NA>
#> 11523 <NA>
#> 11524 <NA>
#> 17583 <NA>
#> 17584 <NA>
#> 17585 <NA>
#> 17586 <NA>
#> 17587 <NA>
#> 17588 <NA>
#> 17589 <NA>
#> 17590 <NA>
#> 17591 <NA>
#> 17592 <NA>
#> 17593 <NA>
#> 17594 <NA>
#> 17595 <NA>
#> 19178 <NA>
#> 19179 <NA>
#> 19180 <NA>
#> 19181 <NA>
#> 19182 <NA>
#> 19183 <NA>
#> 19184 <NA>
#> 19185 <NA>
#> 19186 <NA>
#> 26756 <NA>
#> 26757 <NA>
#> 26758 <NA>
#> 30233 <NA>
#> 31403 <NA>
#> 33026 <NA>
#> 33433 <NA>
#> 34562 <NA>
#> 34563 <NA>
#> 34564 <NA>
#> 34565 <NA>
#> 34566 <NA>
#> 34567 <NA>
#> 34568 <NA>
#> 39888 <NA>
#> 39889 <NA>
#> 39890 <NA>
#> 39891 <NA>
#> 39892 <NA>
#> 39893 <NA>
#> 39894 <NA>
#> 39895 <NA>
#> 39896 <NA>
#> 41884 <NA>
#> 41901 <NA>
#> 42256 <NA>
#> 42257 <NA>
#> 42258 <NA>
#> 42259 <NA>
#> 42260 <NA>
#> 42261 <NA>
#> 42262 <NA>
#> 42263 <NA>
#> 42264 <NA>
#> 42265 <NA>
#> 43220 <NA>
#> 43955 <NA>
#> 43956 <NA>
#> 46558 <NA>
#> 46559 <NA>We have 144 NAN, where
- The zip code was not found in the atmo df
- The zip code was incorectly isolated from the address
Removed them ::: {.cell layout-align=“center”}
Click to show code
#remove the rows with nan in city
properties_filtered <- df[!is.na(df$City),]
#show the first 100 rows of the cleaned dataset using reactable
reactable(head(properties_filtered, 100)):::
2.1.4 Tax data
- ajouter source
- ajouter description
- expliquer blabla
2.1.4.1 Cleaning
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#excel file modified for fribourg and liestal
# read csv
impots <- read.csv(file.path(here(),"data/statistik-steuerfuesse-np-1995-2023-fr.csv"), sep = ",", header = TRUE, stringsAsFactors = FALSE)
# Remove last 13 rows
impots <- head(impots, -14)
# Rename columns
colnames(impots) <- c("Chefs-lieux cantonaux", "Impôt cantonal", "Impôt communal", "Impôt paroissial Evang. / réform", "Impôt paroissial Kath. / cath.rom")
# Set row names to the content of the first column
rownames(impots) <- impots$`Chefs-lieux cantonaux`
# Remove the first column (which is now row names)
impots1 <- impots[, -1, drop = FALSE]
# Set the row names for impots1 the same as impots
rownames(impots1) <- rownames(impots)
impots <- impots1
rm(impots1)
# Write data to CSV based on here()
#write.csv(impots, file.path(here(),"data/impots.csv"), row.names = TRUE)
# show 100 first rows of impots using reactable
reactable(head(impots, 100))2.1.4.2 Merging the two datasets
Click to show code
# # Create a copy of properties_filtered
# properties_merged <- properties_filtered
#
# # Loop through unique canton names in properties_filtered
# for (canton_name in unique(properties_filtered$canton)) {
# # Check if canton_name exists as a row name in impots
# if (canton_name %in% rownames(impots)) {
# # Get the corresponding row from impots
# impots_row <- impots[canton_name, ]
#
# # Add columns to properties_merged with impots content
# for (col_name in names(impots)) {
# properties_merged[properties_merged$canton == canton_name, paste0(col_name, "_impots")] <- impots_row[[col_name]]
# }
#
# # Remove impots_row from memory
# rm(impots_row)
# }
# }
#
# # Write data to CSV based on here()
# write.csv(properties_merged, file.path(here(),"data/properties_merged.csv"), row.names = TRUE)
# properties_filtered <- properties_merged
# # show 100 first rows of properties_merged using reactable
# reactable(head(properties_merged, 100))2.1.5 Commune Data
2.1.5.1 Cleaning
- ajouter source
- ajouter description
- expliquer blabla
Replaces NAs in both Taux de couverture social and Political (Conseil National Datas) For Taux de couverture Social: NAs were due to reason “Q” = “Not indicated to protect confidentiality” We replaced the NAs by the average taux de couverture in Switzerland in 2019, which was 3.2%
For Political data: NAs were due to reason “M” = “Not indicated because data was not important or applicable” Therefore, we replaced the NAs by 0
Click to show code
# il faudra changer le path
commune_prep <- read.csv(file.path(here(),"data/commune_data.csv"), sep = ";", header = TRUE, stringsAsFactors = FALSE)
# We keep only 2019 to have some reference? (2020 is apparently not really complete)
commune_2019 <- subset(commune_prep, PERIOD_REF == "2019") %>%
select(c("REGION", "CODE_REGION", "INDICATORS", "VALUE", "STATUS"))
# delete les lignes ou Status = Q ou M (pas de valeur) et ensuite on enlève la colonne
commune_2019 <- subset(commune_2019, STATUS == "A") %>%
select(c("REGION", "CODE_REGION", "INDICATORS", "VALUE"))
# on enlève les lignes qui sont des aggrégats
commune_2019 <- subset(commune_2019, REGION != "Schweiz")
commune_2019 <- commune_2019 %>%
pivot_wider(names_from = INDICATORS, values_from = VALUE)
# Rename columns using the provided map
df_commune <- commune_2019 %>%
rename(`Population - Habitants` = Ind_01_01,
`Population - Densité de la population` = Ind_01_03,
`Population - Etrangers` = Ind_01_08,
`Population - Part du groupe d'âge 0-19 ans` = Ind_01_04,
`Population - Part du groupe d'âge 20-64 ans` = Ind_01_05,
`Population - Part du groupe d'âge 65+ ans` = Ind_01_06,
`Population - Taux brut de nuptialité` = Ind_01_09,
`Population - Taux brut de divortialité` = Ind_01_10,
`Population - Taux brut de natalité` = Ind_01_11,
`Population - Taux brut de mortalité` = Ind_01_12,
`Population - Ménages privés` = Ind_01_13,
`Population - Taille moyenne des ménages` = Ind_01_14,
`Sécurité sociale - Taux d'aide sociale` = Ind_11_01,
`Conseil national - PLR` = Ind_14_01,
`Conseil national - PDC` = Ind_14_02,
`Conseil national - PS` = Ind_14_03,
`Conseil national - UDC` = Ind_14_04,
`Conseil national - PEV/PCS` = Ind_14_05,
`Conseil national - PVL` = Ind_14_06,
`Conseil national - PBD` = Ind_14_07,
`Conseil national - PST/Sol.` = Ind_14_08,
`Conseil national - PES` = Ind_14_09,
`Conseil national - Petits partis de droite` = Ind_14_10)
# If no one voted for a party, set as NA -> replacing it with 0 instead
df_commune <- df_commune %>%
mutate_at(vars(starts_with("Conseil national")), ~replace_na(., 0))
# Removing NAs from Taux de couverture sociale column
# Setting the mean as the mean for Switzerland in 2019 (3.2%)
mean_taux_aide_social <- 3.2
# Replace NA values with the mean
df_commune <- df_commune %>%
mutate(`Sécurité sociale - Taux d'aide sociale` = if_else(is.na(`Sécurité sociale - Taux d'aide sociale`), mean_taux_aide_social, `Sécurité sociale - Taux d'aide sociale`))
#show 100 first rows of df_commune using reactable
reactable(head(df_commune, 100))Click to show code
# commune_prep <- read.csv(file.path(here(),"data/commune_data.csv"), sep = ";", header = TRUE, stringsAsFactors = FALSE)
#
# # We keep only 2019 to have some reference? (2020 is apparently not really complete)
# commune_2019 <- subset(commune_prep, PERIOD_REF == "2019") %>%
# select(c("REGION", "CODE_REGION", "INDICATORS", "VALUE", "STATUS"))
#
# # delete les lignes ou Status = Q ou M (pas de valeur) et ensuite on enlève la colonne
# commune_2019 <- subset(commune_2019, STATUS == "A") %>%
# select(c("REGION", "CODE_REGION", "INDICATORS", "VALUE"))
#
# # on enlève les lignes qui sont des aggrégats
# commune_2019 <- subset(commune_2019, REGION != "Schweiz")
#
# commune_2019 <- commune_2019 %>%
# pivot_wider(names_from = INDICATORS, values_from = VALUE)
#
# # Rename columns using the provided map
# df_commune <- commune_2019 %>%
# rename(`Population - Habitants` = Ind_01_01,
# `Population - Densité de la population` = Ind_01_03,
# `Population - Etrangers` = Ind_01_08,
# `Population - Part du groupe d'âge 0-19 ans` = Ind_01_04,
# `Population - Part du groupe d'âge 20-64 ans` = Ind_01_05,
# `Population - Part du groupe d'âge 65+ ans` = Ind_01_06,
# `Population - Taux brut de nuptialité` = Ind_01_09,
# `Population - Taux brut de divortialité` = Ind_01_10,
# `Population - Taux brut de natalité` = Ind_01_11,
# `Population - Taux brut de mortalité` = Ind_01_12,
# `Population - Ménages privés` = Ind_01_13,
# `Population - Taille moyenne des ménages` = Ind_01_14,
# `Sécurité sociale - Taux d'aide sociale` = Ind_11_01,
# `Conseil national - PLR` = Ind_14_01,
# `Conseil national - PDC` = Ind_14_02,
# `Conseil national - PS` = Ind_14_03,
# `Conseil national - UDC` = Ind_14_04,
# `Conseil national - PEV/PCS` = Ind_14_05,
# `Conseil national - PVL` = Ind_14_06,
# `Conseil national - PBD` = Ind_14_07,
# `Conseil national - PST/Sol.` = Ind_14_08,
# `Conseil national - PES` = Ind_14_09,
# `Conseil national - Petits partis de droite` = Ind_14_10)
#
# # If no one voted for a party, set as NA -> replacing it with 0 instead
# df_commune <- df_commune %>%
# mutate_at(vars(starts_with("Conseil national")), ~replace_na(., 0))
#
#
# # Removing NAs from Taux de couverture sociale column
# # Setting the mean as the mean for Switzerland in 2019 (3.2%)
# mean_taux_aide_social <- 3.2
#
# # Replace NA values with the mean
# df_commune <- df_commune %>%
# mutate(`Sécurité sociale - Taux d'aide sociale` = if_else(is.na(`Sécurité sociale - Taux d'aide sociale`), mean_taux_aide_social, `Sécurité sociale - Taux d'aide sociale`))
# 3 EDA
3.1 Histogram of prices
Click to show code
histogram_price <- ggplot(properties_filtered, aes(x = price)) +
geom_histogram(binwidth = 100000, fill = "skyblue", color = "red") +
labs(title = "Distribution of Prices",
x = "Price",
y = "Frequency") +
theme_minimal()
# Convert ggplot object to plotly object
interactive_histogram_price <- ggplotly(histogram_price)
# Display the interactive histogram
interactive_histogram_price3.2 Histogram of prices for each property type
note : only price between 0 and 500000 so some outliers aren’t here
Click to show code
# Create the ggplot object
histogram <- ggplot(properties_filtered, aes(x = price)) +
geom_histogram(binwidth = 100000, fill = "skyblue", color = "black") +
facet_wrap(~ property_type, scales = "free", ncol = 2) +
labs(title = "Distribution of Prices by Property Type",
x = "Price",
y = "Frequency") +
theme_minimal() +
xlim(0, 5000000)
# Convert ggplot object to plotly object
interactive_histogram <- ggplotly(histogram)
# Display the interactive plot
interactive_histogram3.3 Histogram of prices for each year category
note : only price between 0 and 500000 so some outliers aren’t here
Click to show code
# Create a histogram of prices for each year category
histogram <- ggplot(properties_filtered, aes(x = price)) +
geom_histogram(binwidth = 100000, fill = "skyblue", color = "black") +
facet_wrap(~ year_category, scales = "free", ncol = 2) +
labs(title = "Distribution of Prices by Year Category",
x = "Price",
y = "Frequency") +
theme_minimal() +
xlim(0, 5000000)
# Convert ggplot object to plotly object
interactive_histogram_year <- ggplotly(histogram)
# Display the interactive plot
interactive_histogram_year3.4 Histogram of prices for each canton
note : only price between 0 and 500000 so some outliers aren’t here
Click to show code
histogram <- ggplot(properties_filtered, aes(x = price)) +
geom_histogram(binwidth = 100000, fill = "skyblue", color = "black") +
facet_wrap(~ canton, scales = "free", ncol = 2) +
labs(title = "Distribution of Prices by Canton",
x = "Price",
y = "Frequency") +
theme_minimal() +
xlim(0, 5000000)
# Convert ggplot object to plotly object with adjusted height
interactive_histogram <- ggplotly(histogram) %>%
layout(height = 1000) # Adjust the height as needed
# Display the interactive plot
interactive_histogram3.5 Histogram of prices for each number of rooms
note : only price between 0 and 500000 so some outliers aren’t here
and the graph below only show apartments with less than 10 rooms (but you can change the code if needed
Click to show code
properties_room <- properties_filtered[properties_filtered$number_of_rooms < 20, ] # Filter only number_of_rooms less than 20
# Create a histogram of prices for each number of rooms
histogram <- ggplot(properties_room, aes(x = price)) +
geom_histogram(binwidth = 100000, fill = "skyblue", color = "black") +
facet_wrap(~ number_of_rooms, scales = "free", ncol = 2) +
labs(title = "Distribution of Prices by Number of Rooms",
x = "Price",
y = "Frequency") +
theme_minimal() +
xlim(0, 5000000)
# Convert ggplot object to plotly object with adjusted height
interactive_histogram <- ggplotly(histogram) %>%
layout(height = 1000) # Adjust the height as needed
# Display the interactive plot
interactive_histogram3.6 Histogram of prices with impot
Click to show code
# colnames(properties_filtered)[(ncol(properties_filtered) - 3):ncol(properties_filtered)] <- gsub("\\s+", "_", colnames(properties_filtered)[(ncol(properties_filtered) - 3):ncol(properties_filtered)])
#
# # Create a scatter plot to visualize correlation between price and Impôt cantonal
# scatter_plot <- ggplot(properties_filtered, aes(x = price, y = Impôt_cantonal_impots)) +
# geom_point() +
# labs(title = "Correlation between Price and Impôt cantonal",
# x = "Price",
# y = "Impôt cantonal") +
# theme_minimal()
#
# # Convert ggplot object to plotly object
# interactive_plot <- ggplotly(scatter_plot)
#
# # Display the interactive plot
# interactive_plot3.7 Test Regression
Click to show code
# Perform multiple linear regression
model <- lm(price ~ number_of_rooms + square_meters + canton + property_type + year_category, data = properties_filtered)
# Summarize the regression model
summary(model)
#>
#> Call:
#> lm(formula = price ~ number_of_rooms + square_meters + canton +
#> property_type + year_category, data = properties_filtered)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -12863879 -318837 -59535 211251 16094914
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) -571974.1 29124.7 -19.64 < 2e-16
#> number_of_rooms -664.1 232.1 -2.86 0.00422
#> square_meters 9670.6 48.7 198.68 < 2e-16
#> cantonAppenzell-Ausser-Rhoden -49354.9 58483.4 -0.84 0.39872
#> cantonAppenzell-Inner-Rhoden 119076.8 119190.4 1.00 0.31778
#> cantonBasel-Landschaft 119188.9 32646.7 3.65 0.00026
#> cantonBasel-Stadt 550598.5 75191.0 7.32 2.5e-13
#> cantonBern -143302.0 25791.4 -5.56 2.8e-08
#> cantonFribourg -138139.4 26509.2 -5.21 1.9e-07
#> cantonGeneva 1369129.7 37877.4 36.15 < 2e-16
#> cantonGlarus -257327.7 97006.6 -2.65 0.00799
#> cantonGrisons 236239.9 45281.4 5.22 1.8e-07
#> cantonJura -634396.0 43838.1 -14.47 < 2e-16
#> cantonLucerne 127650.9 37351.1 3.42 0.00063
#> cantonNeuchatel -227592.3 35855.3 -6.35 2.2e-10
#> cantonNidwalden 446997.2 92461.7 4.83 1.3e-06
#> cantonObwalden 813963.3 115379.6 7.05 1.8e-12
#> cantonSchaffhausen -118840.0 69881.8 -1.70 0.08903
#> cantonSchwyz 430523.3 73325.5 5.87 4.3e-09
#> cantonSolothurn -196911.0 35907.3 -5.48 4.2e-08
#> cantonSt-Gallen -54343.2 30354.3 -1.79 0.07341
#> cantonThurgau -83863.1 32028.8 -2.62 0.00884
#> cantonTicino 51018.3 22919.1 2.23 0.02602
#> cantonUri 116303.2 86335.9 1.35 0.17795
#> cantonValais -43447.8 22514.3 -1.93 0.05364
#> cantonVaud 374336.6 23673.3 15.81 < 2e-16
#> cantonZug 1333285.5 62892.0 21.20 < 2e-16
#> cantonZurich 431451.8 29063.2 14.85 < 2e-16
#> property_typeAttic flat 71410.8 22532.7 3.17 0.00153
#> property_typeBifamiliar house -287182.6 22370.6 -12.84 < 2e-16
#> property_typeChalet 229320.1 24921.8 9.20 < 2e-16
#> property_typeDuplex -213772.6 29049.9 -7.36 1.9e-13
#> property_typeFarm house -463341.0 61245.2 -7.57 3.9e-14
#> property_typeLoft -193955.1 156278.0 -1.24 0.21458
#> property_typeRoof flat -112127.9 33006.5 -3.40 0.00068
#> property_typeRustic house -60336.5 124202.5 -0.49 0.62712
#> property_typeSingle house -224374.4 12767.8 -17.57 < 2e-16
#> property_typeTerrace flat 29723.7 46230.7 0.64 0.52026
#> property_typeVilla -46324.0 20056.1 -2.31 0.02091
#> year_category1919-1945 248287.6 32007.7 7.76 8.9e-15
#> year_category1946-1960 281495.6 29638.4 9.50 < 2e-16
#> year_category1961-1970 238844.7 24197.2 9.87 < 2e-16
#> year_category1971-1980 314981.2 21909.2 14.38 < 2e-16
#> year_category1981-1990 269024.8 22265.8 12.08 < 2e-16
#> year_category1991-2000 436391.4 23034.2 18.95 < 2e-16
#> year_category2001-2005 427219.4 28254.0 15.12 < 2e-16
#> year_category2006-2010 510524.9 24421.0 20.91 < 2e-16
#> year_category2011-2015 549657.7 23882.6 23.02 < 2e-16
#> year_category2016-2024 490791.8 18754.8 26.17 < 2e-16
#>
#> (Intercept) ***
#> number_of_rooms **
#> square_meters ***
#> cantonAppenzell-Ausser-Rhoden
#> cantonAppenzell-Inner-Rhoden
#> cantonBasel-Landschaft ***
#> cantonBasel-Stadt ***
#> cantonBern ***
#> cantonFribourg ***
#> cantonGeneva ***
#> cantonGlarus **
#> cantonGrisons ***
#> cantonJura ***
#> cantonLucerne ***
#> cantonNeuchatel ***
#> cantonNidwalden ***
#> cantonObwalden ***
#> cantonSchaffhausen .
#> cantonSchwyz ***
#> cantonSolothurn ***
#> cantonSt-Gallen .
#> cantonThurgau **
#> cantonTicino *
#> cantonUri
#> cantonValais .
#> cantonVaud ***
#> cantonZug ***
#> cantonZurich ***
#> property_typeAttic flat **
#> property_typeBifamiliar house ***
#> property_typeChalet ***
#> property_typeDuplex ***
#> property_typeFarm house ***
#> property_typeLoft
#> property_typeRoof flat ***
#> property_typeRustic house
#> property_typeSingle house ***
#> property_typeTerrace flat
#> property_typeVilla *
#> year_category1919-1945 ***
#> year_category1946-1960 ***
#> year_category1961-1970 ***
#> year_category1971-1980 ***
#> year_category1981-1990 ***
#> year_category1991-2000 ***
#> year_category2001-2005 ***
#> year_category2006-2010 ***
#> year_category2011-2015 ***
#> year_category2016-2024 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Residual standard error: 922000 on 48028 degrees of freedom
#> Multiple R-squared: 0.557, Adjusted R-squared: 0.557
#> F-statistic: 1.26e+03 on 48 and 48028 DF, p-value: <2e-16
#show the result in the html
kable(summary(model)$coefficients, format = "html") %>%
kable_styling(full_width = F)| Estimate | Std. Error | t value | Pr(>|t|) | |
|---|---|---|---|---|
| (Intercept) | -571974 | 29124.7 | -19.639 | 0.000 |
| number_of_rooms | -664 | 232.1 | -2.861 | 0.004 |
| square_meters | 9671 | 48.7 | 198.677 | 0.000 |
| cantonAppenzell-Ausser-Rhoden | -49355 | 58483.4 | -0.844 | 0.399 |
| cantonAppenzell-Inner-Rhoden | 119077 | 119190.4 | 0.999 | 0.318 |
| cantonBasel-Landschaft | 119189 | 32646.7 | 3.651 | 0.000 |
| cantonBasel-Stadt | 550598 | 75191.0 | 7.323 | 0.000 |
| cantonBern | -143302 | 25791.4 | -5.556 | 0.000 |
| cantonFribourg | -138139 | 26509.2 | -5.211 | 0.000 |
| cantonGeneva | 1369130 | 37877.4 | 36.146 | 0.000 |
| cantonGlarus | -257328 | 97006.6 | -2.653 | 0.008 |
| cantonGrisons | 236240 | 45281.4 | 5.217 | 0.000 |
| cantonJura | -634396 | 43838.1 | -14.471 | 0.000 |
| cantonLucerne | 127651 | 37351.1 | 3.418 | 0.001 |
| cantonNeuchatel | -227592 | 35855.3 | -6.348 | 0.000 |
| cantonNidwalden | 446997 | 92461.7 | 4.834 | 0.000 |
| cantonObwalden | 813963 | 115379.6 | 7.055 | 0.000 |
| cantonSchaffhausen | -118840 | 69881.8 | -1.701 | 0.089 |
| cantonSchwyz | 430523 | 73325.5 | 5.871 | 0.000 |
| cantonSolothurn | -196911 | 35907.3 | -5.484 | 0.000 |
| cantonSt-Gallen | -54343 | 30354.3 | -1.790 | 0.073 |
| cantonThurgau | -83863 | 32028.8 | -2.618 | 0.009 |
| cantonTicino | 51018 | 22919.1 | 2.226 | 0.026 |
| cantonUri | 116303 | 86335.9 | 1.347 | 0.178 |
| cantonValais | -43448 | 22514.3 | -1.930 | 0.054 |
| cantonVaud | 374337 | 23673.3 | 15.813 | 0.000 |
| cantonZug | 1333286 | 62892.0 | 21.200 | 0.000 |
| cantonZurich | 431452 | 29063.2 | 14.845 | 0.000 |
| property_typeAttic flat | 71411 | 22532.7 | 3.169 | 0.002 |
| property_typeBifamiliar house | -287183 | 22370.6 | -12.838 | 0.000 |
| property_typeChalet | 229320 | 24921.8 | 9.202 | 0.000 |
| property_typeDuplex | -213773 | 29049.9 | -7.359 | 0.000 |
| property_typeFarm house | -463341 | 61245.2 | -7.565 | 0.000 |
| property_typeLoft | -193955 | 156278.0 | -1.241 | 0.215 |
| property_typeRoof flat | -112128 | 33006.5 | -3.397 | 0.001 |
| property_typeRustic house | -60336 | 124202.5 | -0.486 | 0.627 |
| property_typeSingle house | -224374 | 12767.8 | -17.573 | 0.000 |
| property_typeTerrace flat | 29724 | 46230.7 | 0.643 | 0.520 |
| property_typeVilla | -46324 | 20056.1 | -2.310 | 0.021 |
| year_category1919-1945 | 248288 | 32007.7 | 7.757 | 0.000 |
| year_category1946-1960 | 281496 | 29638.4 | 9.498 | 0.000 |
| year_category1961-1970 | 238845 | 24197.2 | 9.871 | 0.000 |
| year_category1971-1980 | 314981 | 21909.2 | 14.377 | 0.000 |
| year_category1981-1990 | 269025 | 22265.8 | 12.082 | 0.000 |
| year_category1991-2000 | 436391 | 23034.2 | 18.945 | 0.000 |
| year_category2001-2005 | 427219 | 28254.0 | 15.121 | 0.000 |
| year_category2006-2010 | 510525 | 24421.0 | 20.905 | 0.000 |
| year_category2011-2015 | 549658 | 23882.6 | 23.015 | 0.000 |
| year_category2016-2024 | 490792 | 18754.8 | 26.169 | 0.000 |
Click to show code
impot_cols <- names(properties_filtered)[startsWith(names(properties_filtered), "Impôt")]
# Count the number of NA values in selected columns
na_counts <- colSums(is.na(properties_filtered[impot_cols]))
# Print the counts
print(na_counts)
#> numeric(0)4 Supervised learning
- Data splitting (if a training/test set split is enough for the global analysis, at least one CV or bootstrap must be used)
- Two or more models
- Two or more scores
- Tuning of one or more hyperparameters per model
- Interpretation of the model(s)
5 Unsupervised learning
- Clustering and/or dimension reduction
6 Conclusion
- Brief summary of the project
- Take home message
- Limitations
- Future work?